Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge
This addresses the problem of hardware-specific knowledge gaps in AI for semiconductor researchers and designers, though it is incremental as it adapts an existing model to a new domain.
The paper tackled the challenge of applying large language models to hardware design and verification by introducing Hardware Phi-1.5B, a model tailored for the semiconductor industry, which showed improved performance in these tasks.
In the rapidly evolving semiconductor industry, where research, design, verification, and manufacturing are intricately linked, the potential of Large Language Models to revolutionize hardware design and security verification is immense. The primary challenge, however, lies in the complexity of hardware specific issues that are not adequately addressed by the natural language or software code knowledge typically acquired during the pretraining stage. Additionally, the scarcity of datasets specific to the hardware domain poses a significant hurdle in developing a foundational model. Addressing these challenges, this paper introduces Hardware Phi 1.5B, an innovative large language model specifically tailored for the hardware domain of the semiconductor industry. We have developed a specialized, tiered dataset comprising small, medium, and large subsets and focused our efforts on pretraining using the medium dataset. This approach harnesses the compact yet efficient architecture of the Phi 1.5B model. The creation of this first pretrained, hardware domain specific large language model marks a significant advancement, offering improved performance in hardware design and verification tasks and illustrating a promising path forward for AI applications in the semiconductor sector.